25 NCAAM Triangles Part 2
First Ball
I’ve been doing a bunch of Triangle analysis here in the spring.
24 NCAA Women’s Analysis
25 LOVB Analysis
What’s still left on the calendar? The NCAA Men’s season, plus the PVF. Of course, there’s also European Champions League and other international competitions. I might be able to get some analysis done on those championships, but… there’s only so much time and space without SmarterVolley turning into nothing but match analysis posts. So we’ll see.
But for now, men’s volleyball! As a reminder, if you are unfamiliar with the Triangle analysis framework or some of the other terminology I use here, check our the Triangle Primer that I updated for this year. The data that I used was from the top-20 teams, plus playoff qualifiers Belmont Abbey, Penn State, and Daemen. I didn’t include Fort Valley St because they don’t have enough matches up on Volleymetrics.
2025 NCAA Men - First Ball
Let’s take a look at how First Ball Differential influenced winning in this year’s NCAA Men’s Volleyball season.
As you might expect, the correlation here is strong to winning. Dominating the First Ball portion of the game generally leads to winning a lot.
FB-related Correlations
0.73 - First Ball Differential
0.77 - First Ball Efficiency
0.53 - Opponent First Ball Efficiency
What’s interesting is that First Ball Efficiency actually had a higher correlation than First Ball Differential. Again, we see a similar issue when looking at season-level data in a high sideout environment. For example, compare UCSB and UCI.
First Ball Efficiency
0.356 - UCI
0.306 - UCSB
Opponent Efficiency
0.306 - UCI
0.294 - UCSB
UCI has a 50-point advantage in efficiency while UCSB only had a 12-point advantage. But yet UCSB won 52% of FB points while UCI won 50%. Why? Two reasons:
UCI won more points overall due to Terminal Serving and Transition. So they were serving more often than UCSB. Since the receiving team has a strong advantage in men’s volleyball, serving a lot depresses your FB% a bit.
UCSB missed 5% more serves than UCI and their opponents missed 2% fewer serves than UCI’s opponents. This means UCSB has an additional 7% more sideout opportunities relative to UCI.
In an individual match, particularly one that’s relatively even, the raw FB Differential works really well, but normalizing to efficiency works a bit better. That differential has a correlation of 0.84, higher than any of the other metrics.
Offense v Defense
In this season, FB Offense (0.77 correlation) was significantly more correlated with winning than FB Defense (-0.53 correlation). Let’s look at those charts:
If you’re above the line (ex: Mount Olive, Lincoln Memorial), you won more than your offensive ability would suggest. If you’re below the line (ex: UCSB, Ball St), you lost more than your offensive ability would suggest.
Visually, you can see the there’s more teams diverging from the trendline in the Opponent Efficiency graph than in the Efficiency graph. Princeton, Daemen, Stanford, and UCSB were all better defensively than their record would suggest. Loyola, Hawaii, and UC Irvine stand out as teams that won more than their defense would suggest.
Several of these trends suggest that UCSB was either unlucky or un-clutch (depending on how much you believe in clutch play) in that, statistically, they were a lot more like a 12-14 or 13-13 team than a 10-16 team.
On Friday I’ll post up the Transition stats as well as a bit of a deeper dive into some of the playoff matchups.